Update

docs/src/changelog.md

@@ -596,7 +596,7 @@ changes which might be breaking for a very small number of users.
  - Clarified the documentation to say that matrices in [`HermitianPSDCone`](@ref)
  must be `LinearAlgebra.Hermitian` (#3241)
  - Minor style fixes to internal macro code (#3247)
- - Add [Quantum state discrimination](@ref) tutorial (#3250)
+ - Add [Example: quantum state discrimination](@ref) tutorial (#3250)
  - Improve error message when `begin...end` not passed to plural macros (#3255)
  - Document how to register function with varying number of input arguments
  (#3258)
@@ -653,12 +653,12 @@ changes which might be breaking for a very small number of users.
 
  - Minor fixes to the documentation (#3200) (#3201) (#3203) (#3210)
  - Added tutorial [Constraint programming](@ref) (#3202)
- - Added more examples to [Tips and Tricks](@ref conic_tips_and_tricks)
+ - Added more examples to [Modeling with cones](@ref)
  - Remove `_distance_to_set` in favor of `MOI.Utilities.distance_to_set` (#3209)
  - Improve [The diet problem](@ref) tutorial by adding the variable as a column
  in the dataframe (#3213)
  - Improve [The knapsack problem example](@ref) tutorial (#3216) (#3217)
- - Added the [Ellipsoid approximation](@ref) tutorial (#3218)
+ - Added the [Example: ellipsoid approximation](@ref) tutorial (#3218)
 
 ## Version 1.7.0 (January 25, 2023)
 

docs/src/manual/complex.md

@@ -11,7 +11,7 @@ DocTestFilters = [r"≤|<=", r"≥|>=", r" == | = ", r" ∈ | in ", r"MathOptInt
 
 This page explains the complex-valued variables and constraints that JuMP
 supports. For a worked-example using these features, read the
-[Quantum state discrimination](@ref) tutorial.
+[Example: quantum state discrimination](@ref) tutorial.
 
 ## Complex-valued variables
 

docs/src/manual/models.md

@@ -655,7 +655,7 @@ MOIU.CachingOptimizer
 Bridges can be added and removed from a [`MOI.Bridges.LazyBridgeOptimizer`](@ref)
 using [`add_bridge`](@ref) and [`remove_bridge`](@ref). Use
 [`print_active_bridges`](@ref) to see which bridges are used to reformulate the
-model. Read the [Ellipsoid approximation](@ref) tutorial for more details.
+model. Read the [Example: ellipsoid approximation](@ref) tutorial for more details.
 
 ### Unsafe backend
 

docs/src/tutorials/applications/optimal_power_flow.jl

@@ -20,7 +20,7 @@
 # matrix cones such as the [`HermitianPSDCone`](@ref) object.
 
 # For another example of modeling with complex decision variables, see the
-# [Quantum state discrimination](@ref) tutorial, and see the
+# [Example: quantum state discrimination](@ref) tutorial, and see the
 # [Complex number support](@ref) section of the manual for more details.
 
 # This tutorial takes a matrix-oriented approach focused on network nodes

docs/src/tutorials/conic/ellipse_approx.jl

@@ -12,7 +12,7 @@
 
 # The model comes from Section 4.9 of [BenTal2001](@cite).
 
-# For a related example, see also the [Minimal ellipses](@ref) tutorial.
+# For a related example, see also the [Example: minimal ellipses](@ref) tutorial.
 
 # ## Required packages
 

docs/src/tutorials/conic/introduction.md

@@ -39,12 +39,11 @@ will help you know where to look for certain things.
  then formulate it in mathematics, and then solve it in JuMP. This usually
  involves some sort of visualization of the solution. Start here if you are
  new to JuMP.
- * [Experiment design](@ref)
- * [Logistic regression](@ref)
- * The [Tips and tricks](@ref conic_tips_and_tricks) tutorial contains a
- number of helpful reformulations and tricks you can use when modeling
- conic programs. Look here if you are stuck trying to formulate a problem
- as a conic program.
+ * [Example: experiment design](@ref)
+ * [Example: logistic regression](@ref)
+ * The [Modeling with cones](@ref) tutorial contains a number of helpful
+ reformulations and tricks you can use when modeling conic programs. Look here
+ if you are stuck trying to formulate a problem as a conic program.
  * The remaining tutorials are less verbose and styled in the form of short code
  examples. These tutorials have less explanation, but may contain useful
  code snippets, particularly if they are similar to a problem you are trying

docs/src/tutorials/conic/tips_and_tricks.jl

@@ -18,7 +18,7 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE #src
 # SOFTWARE. #src
 
-# # [Modeling with cones](@id conic_tips_and_tricks)
+# # Modeling with cones
 
 # **This tutorial was originally contributed by Arpit Bhatia.**
 

docs/src/tutorials/transitioning/transitioning_from_matlab.jl

@@ -127,10 +127,10 @@ m[2]
 # | `@constraint(model, [t; v] in SecondOrderCone())` | `cone(v, t)` | `{v, t} == lorentz(length(v))` |
 # | `@constraint(model, [x, y, z] in MOI.ExponentialCone())` | `expcone([x, y, z])` | `{x, y, z} == exponential(1)` |
 
-# Like YALMIP and CVX, JuMP is smart enough to not generate redundant 
-# constraints when declaring equality constraints between `Symmetric` or 
-# `Hermitian` matrices. In these cases `@constraint(model, m == c)` will not 
-# generate constraints for the lower diagonal and the imaginary part of the 
+# Like YALMIP and CVX, JuMP is smart enough to not generate redundant
+# constraints when declaring equality constraints between `Symmetric` or
+# `Hermitian` matrices. In these cases `@constraint(model, m == c)` will not
+# generate constraints for the lower diagonal and the imaginary part of the
 # diagonal (in the complex case).
 
 # Experienced MATLAB users will probably be relieved to see that you must pass
@@ -233,7 +233,7 @@ objective_value(model)
 # JuMP does no such thing: it only reformulates objectives into objectives, and
 # constraints into constraints, and is fairly conservative at that. As a result,
 # you might need to do some reformulations manually, for which a good guide is
-# the [Tips and tricks](@ref conic_tips_and_tricks) tutorial.
+# the [Modeling with cones](@ref) tutorial.
 
 # ## Vectorization